Household air conditioner interaction ability evaluation method, system, equipment and medium

Abstract

The invention discloses a household air conditioner interaction ability evaluation method, system and device and a medium. The method comprises the steps that evaluation data matrixes of indexes of a to-be-evaluated air conditioner of multiple evaluation objects and technical parameters of the to-be-evaluated air conditioner are obtained; calculating by utilizing a variable coefficient correction method based on the evaluation data matrix to obtain the subjective weight of each index; calculating by using an entropy weight method based on the evaluation data matrix to obtain an objective weight of each index; calculating by utilizing a game theory weighting method based on the subjective weight and the objective weight to obtain a comprehensive weight of each index; evaluating based on the comprehensive weight of each index and the technical parameters of the to-be-evaluated air conditioner to obtain an evaluation result; the subjective weight is determined more accurately through the variable coefficient correction method; the objective weight is determined through the entropy weight method, so that the weighting is more objective, and the credibility is higher; the subjective weight and the objective weight are considered through game theory combination weighting, and a more reasonable comprehensive weight is obtained; and the interaction capability of different types of household air conditioners can be evaluated through the comprehensive weight.

Description

Technical Field

[0001] The present application relates to the technical field of demand-side management, and in particular to a method, system, device and medium for evaluating the interactive capability of a household air conditioner. Background Art

[0002] As an indispensable part of modern families, household air conditioners not only ensure a comfortable indoor environment, but also have great potential to participate in the interactive business of the power grid. With the continuous development of smart grid and distributed energy technology, household air conditioners participating in the interactive business of the power grid represented by demand response can maintain the balance of the power grid, absorb clean energy, and improve the energy efficiency of loads. However, the current research on the issue of household air conditioners participating in the interaction mainly focuses on the formulation of control methods. There are few literatures and standards for the classification and certification of household air conditioners' participation in demand response capabilities. There is no complete evaluation system for how to evaluate the differences in the capabilities of different models of household air conditioners in the interaction of the power grid. This not only affects the healthy development of the home appliance market, but also restricts the overall progress of smart grid construction. Therefore, how to evaluate the differences in the capabilities of different models of household air conditioners in the interaction of the power grid is an urgent problem to be solved. Summary of the invention

[0003] In order to solve the problem of how to evaluate the ability differences of different models of household air conditioners in grid interaction in the prior art, this application proposes a household air conditioner interaction ability evaluation method, including:

[0004] Obtaining evaluation data matrices of various indicators of the air conditioner to be evaluated by multiple evaluation objects and technical parameters of the air conditioner to be evaluated;

[0005] Based on the evaluation data matrix, the subjective weight of each indicator is calculated using the coefficient of variation correction method;

[0006] Based on the evaluation data matrix, the entropy weight method is used to calculate and obtain the objective weight of each indicator;

[0007] The subjective weight of each indicator and the objective weight of each indicator are calculated using the game theory weighting method to obtain the comprehensive weight of each indicator;

[0008] The interactive capability of the air conditioner to be evaluated is evaluated based on the comprehensive weights of the indicators and the technical parameters of the air conditioner to be evaluated to obtain an evaluation result.

[0009] Preferably, the calculation based on the evaluation data matrix using the coefficient of variation correction method to obtain the subjective weight of each indicator includes:

[0010] Performing standardization processing based on the evaluation data matrix to obtain a standardized matrix;

[0011] Calculate the standard deviation and mean value based on the standardized matrix to obtain the standard deviation and mean value of each indicator;

[0012] The coefficient of variation of each indicator was calculated based on the standard deviation and mean value of each indicator to obtain the coefficient of variation of each indicator;

[0013] Based on the coefficient of variation of each indicator, the importance ratio of each indicator to the adjacent indicators is obtained;

[0014] The subjective weight of each indicator is calculated based on the set parameters and the importance ratio of each indicator to its adjacent indicators.

[0015] Preferably, the calculation formula of the coefficient of variation is as follows:

[0016]

[0017] In the formula, C i is the coefficient of variation of the i-th indicator; σ i is the standard deviation of the ith indicator; is the average value of the i-th indicator; i is the ranking of the indicator;

[0018] The calculation formula of the importance ratio of each indicator to the adjacent indicators is as follows:

[0019]

[0020] In the formula, r i is the importance ratio of the ith index to its adjacent indexes; C i-1 is the coefficient of variation of the i-1th indicator; C i is the coefficient of variation of the i-th indicator; i is the ranking of the indicator;

[0021] The calculation formula of the subjective weight of each indicator is as follows:

[0022]

[0023] In the formula, w i is the objective weight of the ith indicator; r o is a parameter; m is the total number of indicators; i is the ranking of indicators;

[0024] w i-1 =r i w i

[0025] In the formula, w i-1 is the subjective weight of the i-1th indicator; r i is the importance ratio between the ith index and its adjacent indexes; w i is the subjective weight of the ith indicator; i is the ranking of the indicator.

[0026] Preferably, the entropy weight method is used to calculate the objective weight of each indicator based on the evaluation data matrix, including:

[0027] Performing standardization processing based on the evaluation data matrix to obtain a standardized matrix;

[0028] Based on the standardized matrix and each indicator, the proportion of each indicator is calculated to obtain the proportion of each indicator;

[0029] Calculate the evaluation value based on the weight of each indicator to obtain the evaluation value of each indicator;

[0030] The entropy weight is calculated based on the evaluation value of each indicator to obtain the objective weight of each indicator.

[0031] Preferably, the calculation formula of the proportion of each indicator is as follows:

[0032]

[0033] In the formula, C ij is the weight of the ith indicator; X ij is the standard value of the jth evaluation object for the ith indicator; j is the ranking of the evaluation object; n is the total number of evaluation objects;

[0034] The calculation formula of the evaluation value of each indicator is as follows:

[0035]

[0036] In the formula, h i is the evaluation value of the ith indicator; C ij is the weight of the i-th indicator; j is the ranking of the evaluation objects; n is the total number of evaluation objects;

[0037] The calculation formula of the objective weight of each indicator is as follows:

[0038]

[0039] In the formula, ε i is the objective weight of the i-th indicator; i is the indicator ranking; m is the total number of indicators; h i is the evaluation value of the ith indicator.

[0040] Preferably, the subjective weights based on the indicators and the objective weights of the indicators are calculated using a game theory weighting method to obtain a comprehensive weight of the indicators, including:

[0041] Determining a weight vector of each indicator based on the subjective weight of each indicator and the objective weight of each indicator;

[0042] Determine each linear combination coefficient based on the combination formula constructed based on the weight vector of each indicator;

[0043] Perform normalization processing based on each linear combination coefficient to obtain the optimal combination coefficient;

[0044] The optimal combination coefficient is combined with the weight vector of each indicator to calculate and obtain the comprehensive weight of each indicator.

[0045] Preferably, the process of determining the indicators of the air conditioner to be evaluated includes:

[0046] Based on the acquired original indicator set affecting the air conditioning demand response capability to be evaluated, a random sample point, a nearest neighbor sample of the same type as the sample point, and a nearest neighbor sample of a different type from the sample point are selected, and a characteristic weight calculation formula is used to calculate to obtain a weight value of each indicator in the original indicator set;

[0047] Screening is performed based on the weight value of each indicator in the original indicator set to obtain each indicator of the air conditioner to be evaluated.

[0048] Preferably, the feature weight calculation formula is as follows:

[0049]

[0050] In the formula, W[A i ] is the weight of the i-th feature; A i is the i-th feature; R i is the sample drawn; H j is the jth sample of the same type extracted; a is the number of iterations; b is the number of samples of the same and different types selected; C is the total number of classes different from sample R; M j (c) is the jth nearest neighbor sample of sample R in class C.

[0051] Based on the same invention application concept, this application also proposes a household air conditioner interactive capability evaluation system, including:

[0052] A data acquisition module, used to obtain the evaluation data matrix of various indicators of the air conditioner to be evaluated by multiple evaluation objects and the technical parameters of the air conditioner to be evaluated;

[0053] A subjective weight calculation module, used to calculate the subjective weight of each indicator based on the evaluation data matrix using a coefficient of variation correction method;

[0054] An objective weight calculation module, used to calculate the objective weight of each indicator based on the evaluation data matrix using the entropy weight method;

[0055] A comprehensive weight calculation module, used to calculate the subjective weight of each indicator and the objective weight of each indicator using a game theory weighting method to obtain the comprehensive weight of each indicator;

[0056] The interactive capability evaluation module is used to evaluate the interactive capability of the air conditioner to be evaluated based on the comprehensive weights of the indicators and the technical parameters of the air conditioner to be evaluated, and obtain an evaluation result.

[0057] Preferably, the subjective weight calculation module is specifically used for:

[0058] Performing standardization processing based on the evaluation data matrix to obtain a standardized matrix;

[0059] Calculate the standard deviation and mean value based on the standardized matrix to obtain the standard deviation and mean value of each indicator;

[0060] The coefficient of variation of each indicator was calculated based on the standard deviation and mean value of each indicator to obtain the coefficient of variation of each indicator;

[0061] Based on the coefficient of variation of each indicator, the importance ratio of each indicator to the adjacent indicators is obtained;

[0062] The subjective weight of each indicator is calculated based on the set parameters and the importance ratio of each indicator to its adjacent indicators.

[0063] Preferably, the calculation formula of the coefficient of variation in the subjective weight calculation module is as follows:

[0064]

[0065] In the formula, C i is the coefficient of variation of the i-th indicator; σ i is the standard deviation of the ith indicator; is the average value of the i-th indicator; i is the ranking of the indicator;

[0066] The calculation formula of the importance ratio of each indicator to the adjacent indicators is as follows:

[0067]

[0068] In the formula, r i is the importance ratio of the ith index to its adjacent indexes; C i-1 is the coefficient of variation of the i-1th indicator; C i is the coefficient of variation of the i-th indicator; i is the ranking of the indicator;

[0069] The calculation formula of the subjective weight of each indicator is as follows:

[0070]

[0071] In the formula, w i is the objective weight of the ith indicator; r o is a parameter; m is the total number of indicators; i is the ranking of indicators;

[0072] w i-1 =r i w i

[0073] In the formula, w i-1 is the subjective weight of the i-1th indicator; r i is the importance ratio between the ith index and its adjacent indexes; w i is the subjective weight of the ith indicator; i is the ranking of the indicator.

[0074] Preferably, the objective weight calculation module is specifically used for:

[0075] Performing standardization processing based on the evaluation data matrix to obtain a standardized matrix;

[0076] Based on the standardized matrix and each indicator, the proportion of each indicator is calculated to obtain the proportion of each indicator;

[0077] Calculate the evaluation value based on the weight of each indicator to obtain the evaluation value of each indicator;

[0078] The entropy weight is calculated based on the evaluation value of each indicator to obtain the objective weight of each indicator.

[0079] Preferably, the calculation formula for the weight of each indicator in the objective weight calculation module is as follows:

[0080]

[0081] In the formula, C ij is the weight of the ith indicator; X ij is the standard value of the jth evaluation object for the ith indicator; j is the ranking of the evaluation object; n is the total number of evaluation objects;

[0082] The calculation formula of the evaluation value of each indicator is as follows:

[0083]

[0084] In the formula, h i is the evaluation value of the ith indicator; C ij is the weight of the i-th indicator; j is the ranking of the evaluation objects; n is the total number of evaluation objects;

[0085] The calculation formula of the objective weight of each indicator is as follows:

[0086]

[0087] In the formula, ε i is the objective weight of the i-th indicator; i is the indicator ranking; m is the total number of indicators; h i is the evaluation value of the ith indicator.

[0088] Preferably, the comprehensive weight calculation module is specifically used for:

[0089] Determining a weight vector of each indicator based on the subjective weight of each indicator and the objective weight of each indicator;

[0090] Determine each linear combination coefficient based on the combination formula constructed based on the weight vector of each indicator;

[0091] Perform normalization processing based on each linear combination coefficient to obtain the optimal combination coefficient;

[0092] The optimal combination coefficient is combined with the weight vector of each indicator to calculate and obtain the comprehensive weight of each indicator.

[0093] Preferably, the process of determining the indicators of the air conditioner to be evaluated in the data acquisition module includes:

[0094] Based on the acquired original indicator set affecting the air conditioning demand response capability to be evaluated, a random sample point, a nearest neighbor sample of the same type as the sample point, and a nearest neighbor sample of a different type from the sample point are selected, and a characteristic weight calculation formula is used to calculate to obtain a weight value of each indicator in the original indicator set;

[0095] Screening is performed based on the weight value of each indicator in the original indicator set to obtain each indicator of the air conditioner to be evaluated.

[0096] Preferably, the feature weight calculation formula is as follows:

[0097]

[0098] In the formula, W[A i ] is the weight of the i-th feature; A i is the i-th feature; R i is the sample drawn; H j is the jth sample of the same type extracted; a is the number of iterations; b is the number of samples of the same and different types selected; C is the total number of classes different from sample R; M j (c) is the jth nearest neighbor sample of sample R in class C.

[0099] On the other hand, the present application also proposes an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0100] The memory is used to store one or more programs;

[0101] When the one or more programs are executed by the at least one processor, a household air conditioner interactive capability evaluation method as described above is implemented.

[0102] On the other hand, the present application also proposes a readable storage medium having an execution program stored thereon, and when the execution program is executed, a household air conditioner interactive capability evaluation method as described above is implemented.

[0103] Compared with the prior art, the beneficial effects of this application are:

[0104] A method, system, device and medium for evaluating the interactive ability of a household air conditioner, comprising: obtaining evaluation data matrices of various indicators of an air conditioner to be evaluated and technical parameters of the air conditioner to be evaluated by multiple evaluation objects; calculating based on the evaluation data matrix using a coefficient of variation correction method to obtain a subjective weight of each indicator; calculating based on the evaluation data matrix using an entropy weight method to obtain an objective weight of each indicator; calculating based on the subjective weight of each indicator and the objective weight of each indicator using a game theory weighting method to obtain a comprehensive weight of each indicator; evaluating the interactive ability of the air conditioner to be evaluated based on the comprehensive weight of each indicator and the technical parameters of the air conditioner to be evaluated to obtain an evaluation result; the subjective weight determined by the coefficient of variation correction method in the present application is more accurate; the objective weight is determined by the entropy weight method, and the weighting is more objective and has a higher credibility; the subjective weight and the objective weight are taken into account by the game theory combined weighting to obtain a more reasonable comprehensive weight; the interactive ability of household air conditioners of different models can be evaluated by the comprehensive weight;

[0105] The various indicators determined by the feature weight algorithm in this application can better evaluate the interactive capability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0106] Figure 1 A flow chart of a household air conditioner interactive capability evaluation method for this application;

[0107] Figure 2 Construct a flow chart for the household air conditioner interactive capability index system of this application;

[0108] Figure 3 This is a diagram of the household air conditioner interactive capability index system structure for this application;

[0109] Figure 4 This is a three-level indicator diagram of the interactive capability of household air conditioners in this application;

[0110] Figure 5 This is the standard diagram for scoring the gear adjustment index for this application;

[0111] Figure 6 This is a structural diagram of a household air conditioner interactive capability evaluation system for this application;

[0112] Figure 7 This is a structural diagram of an electronic device of the present application. DETAILED DESCRIPTION

[0113] At present, theoretical research on household air conditioners participating in grid interaction mainly focuses on the formulation of control strategies and the analysis of adjustable potential. Some demand response identification certification has been carried out, but due to differences in electricity price mechanisms, response scenarios, interaction modes, communication protocols, etc., the standard system for certifying the ability of household appliances to interact with the grid has not yet been established. This invention application proposes a method for evaluating the interactive ability of household air conditioners, which provides a reference for carrying out voluntary demand response identification certification business for household air conditioners, thereby promoting efficient interaction between household air conditioners and the grid. In order to better understand this application, the content of this application is further explained below in conjunction with the drawings and embodiments of the specification.

[0114] Embodiment 1:

[0115] A method for evaluating the interactive capability of household air conditioners, the specific process is as follows Figure 1 As shown, including:

[0116] Step 1, obtaining evaluation data matrices of various indicators of the air conditioner to be evaluated and technical parameters of the air conditioner to be evaluated by multiple evaluation objects;

[0117] Step 2, calculating the subjective weight of each indicator using the coefficient of variation correction method based on the evaluation data matrix;

[0118] Step 3, using the entropy weight method to calculate based on the evaluation data matrix to obtain the objective weight of each indicator;

[0119] Step 4, calculating the subjective weight of each indicator and the objective weight of each indicator using a game theory weighting method to obtain a comprehensive weight of each indicator;

[0120] Step 5: Evaluate the interactive capability of the air conditioner to be evaluated based on the comprehensive weights of the indicators and the technical parameters of the air conditioner to be evaluated to obtain an evaluation result.

[0121] Combine the following Figure 2 The process shown describes this implementation in detail.

[0122] In step 1, obtaining the evaluation data matrix of each index of the air conditioner to be evaluated and the technical parameters of the air conditioner to be evaluated of multiple evaluation objects specifically includes:

[0123] Obtaining evaluation data matrices of various indicators of the air conditioners to be evaluated by multiple evaluation objects;

[0124] Among them, multiple evaluation objects include but are not limited to experts in the field of demand response and experts from air-conditioning manufacturers.

[0125] Obtain technical parameters of the air conditioner to be evaluated;

[0126] Among them, technical parameters include but are not limited to: interruption control signal, control decision signal, electricity price signal, reduction signal, centralized control method, decentralized control method, new energy consumption, peak shaving and valley filling, frequency regulation, standby optimization, optional participation, midway exit, adjustable mode, wifi connection, controller connection, benefit analysis, self-decision-making, hosting and self-control.

[0127] In step 2, the subjective weight of each indicator is obtained by calculating based on the evaluation data matrix using the coefficient of variation correction method, which specifically includes:

[0128] Rank the importance of each indicator;

[0129] Performing standardization processing based on the evaluation data matrix to obtain a standardized matrix;

[0130] Calculate the standard deviation and mean value based on the standardized matrix to obtain the standard deviation and mean value of each indicator;

[0131] The coefficient of variation is calculated based on the standard deviation and mean value of each indicator to obtain the coefficient of variation of each indicator. The calculation formula is as follows:

[0132]

[0133] In the formula, C i is the coefficient of variation of the i-th indicator; σ i is the standard deviation of the ith indicator; is the average value of the i-th indicator; i is the ranking of the indicator;

[0134] Based on the coefficient of variation of each indicator, the importance ratio of each indicator to the adjacent indicators is calculated. The calculation formula is as follows:

[0135]

[0136] In the formula, r i is the importance ratio of the ith index to its adjacent indexes; C i-1 is the coefficient of variation of the i-1th indicator; C i is the coefficient of variation of the i-th indicator; i is the ranking of the indicator;

[0137] Based on the set parameters and the importance ratio of each indicator to its adjacent indicators, the subjective weight of each indicator is calculated. The calculation formula is as follows:

[0138]

[0139] In the formula, w i is the objective weight of the ith indicator; r ois a parameter; m is the total number of indicators; i is the ranking of indicators;

[0140] w i-1 =r i w i

[0141] In the formula, w i-1 is the subjective weight of the i-1th indicator; r i is the importance ratio between the ith index and its adjacent indexes; w i is the subjective weight of the ith indicator; i is the ranking of the indicator.

[0142] In step 3, the objective weight of each indicator is obtained by calculating based on the evaluation data matrix using the entropy weight method, which specifically includes:

[0143] Based on the evaluation data matrix, standardization is performed to obtain a standardized matrix D=(X ij ) m*n ;

[0144] The normalization process is as follows:

[0145] Positive indicator normalization:

[0146]

[0147] Negative indicator normalization:

[0148]

[0149] Where D is the normalized matrix; X ij is the standard value of the jth evaluation object for the i-th indicator; m is the row of the standardization matrix; n is the column of the standardization matrix; c ij is the original data of the i-th indicator of the j-th evaluation object; min(c i ) is the minimum value of the original data of the ith indicator among all evaluation objects; max(c i ) is the maximum value of the original data of the ith indicator among all evaluation objects.

[0150] Based on the standardized matrix and each indicator, the proportion of each indicator is calculated, and the calculation formula is as follows:

[0151]

[0152] In the formula, C ij is the weight of the ith indicator; X ij is the standard value of the jth evaluation object for the ith indicator; j is the ranking of the evaluation object; n is the total number of evaluation objects;

[0153] The evaluation value is calculated based on the weight of each indicator to obtain the evaluation value of each indicator. The calculation formula is as follows:

[0154]

[0155] In the formula, h i is the evaluation value of the ith indicator; C ij is the weight of the i-th indicator; j is the ranking of the evaluation objects; n is the total number of evaluation objects;

[0156] Based on the evaluation value of each indicator, entropy weight calculation is performed to obtain the objective weight of each indicator. The calculation formula is as follows:

[0157]

[0158] And ε i satisfy

[0159] In the formula, ε i is the objective weight of the i-th indicator; i is the indicator ranking; m is the total number of indicators; h i is the evaluation value of the ith indicator.

[0160] In step 4, the subjective weight of each indicator and the objective weight of each indicator are calculated using the game theory weighting method to obtain the comprehensive weight of each indicator, which specifically includes:

[0161] Assuming that m methods are used to calculate the weights of each evaluation index, the weight set can be obtained as follows: W x =[W x1 ,W x2 ,…,W xn ], where x = 1, 2…, m; W x is the weight set obtained by each method; W x1 is the weight vector of the first indicator obtained; W x2 is the weight vector of the second indicator; W xn is the weight vector of the obtained nth indicator.

[0162] The arbitrary combination of m weight vectors thus formed is: where a k is the linear combination coefficient, W is the set of all vectors; is the weight vector obtained by the kth method.

[0163] The optimal strategy model of the combination coefficient is obtained as follows:

[0164]

[0165] In the formula, a j is the combination coefficient; is the weight vector obtained by the jth method; is the weight vector obtained by the ith method.

[0166] The above formula needs to satisfy the optimal first-order derivative condition:

[0167]

[0168] Where W1 is the weight vector obtained by the first method; W m is the weight vector obtained by the mth method; Transpose the weight vector obtained by the first method; is the transpose of the weight vector obtained by the mth method; a1 is the vector coefficient of the first method; a m is the vector coefficient of the mth method. The linear combination coefficients (a1, a2, ..., a m ), and normalize the result, that is,

[0169] Finally, the combined weight

[0170] In the formula, is the optimal combination coefficient; W * is the final combined weight; Transpose the weight vector obtained by the x-th method.

[0171] The process of determining the indicators of the air conditioner to be evaluated specifically includes:

[0172] Get the original indicator data set D = {(x1,y1),(x2,y2),...,(x n ,y n )},x i ∈R P is the feature space of the data, p is the number of features; y i ∈R I is the class label space of the data, and I is the number of class labels. At the same time, the feature weight vector is initialized;

[0173] Randomly select a sample point R i , find k nearest neighbor samples of the same type, and then find k nearest neighbor samples of different types.

[0174] Update the feature weights, the formula is as follows:

[0175]

[0176] In the formula, W[A i ] is the weight of the i-th feature; A i is the i-th feature; R iis the sample drawn; H j is the jth sample of the same type extracted; a is the number of iterations; b is the number of samples of the same and different types selected; C is the total number of classes different from sample R; M j (c) is the jth nearest neighbor sample of sample R in class C.

[0177] Repeat the above operation to update the weights, and use the weights to filter out the indicators that contribute most to the target.

[0178] The final household air conditioner interactive capability index system is as follows: Figure 3 As shown; the first layer is the overall evaluation index of the air conditioner's interactive ability; the second layer is three first-level indicators, namely the participation level, adjustment level, and intelligence level of the air conditioner in demand response. The third layer is the sub-indicators of the three first-level indicators in the second layer, which are used to specifically evaluate the first-level indicators to which they belong. Among them, the participation level indicators include: responsive signal type, interactive scene, and control method; the adjustment level indicators include: flexibility and adjustment gear; the intelligence level indicators include: connection method, self-learning ability, and response mode. The fourth layer is the third-level indicators, as shown below. Figure 4 shown.

[0179] In step 5, the interactive capability of the air conditioner to be evaluated is evaluated based on the comprehensive weights of the indicators and the technical parameters of the air conditioner to be evaluated, and an evaluation result is obtained, which specifically includes:

[0180] The responsive signal type indicates the demand response signal that the air conditioner can receive, mainly including interruption control signal, control decision signal, electricity price signal and reduction signal. When the air conditioner can accept one of the above signals, the signal index is 100 points, otherwise it is zero. Finally, the comprehensive score of the four signals is multiplied and summed by the comprehensive weight of the index to obtain the final score of the responsive signal.

[0181] The interactive scenario indicates the interactive categories that the air-conditioning equipment can participate in, mainly including new energy consumption, peak shaving and valley filling, frequency regulation, and standby optimization. When the air-conditioning equipment can participate in one of the above interactive scenarios, the index is 100 points, otherwise it is zero. Finally, the scores of the four interactive scenario indicators are multiplied and summed by the comprehensive weight of the indicators to obtain the final score of the interactive scenario.

[0182] The control mode indicates the implementation method of demand response in the process of air conditioner participating in power grid regulation. It includes centralized control and decentralized control. When the air conditioner has one of the above control modes, the index score of this mode is 100, otherwise it is zero. If there are two control modes at the same time, that is, mixed control mode, the scores of both indicators are 100. Finally, the final score of the control mode is obtained by multiplying the scores of the two control modes and the comprehensive weight of the indicators.

[0183] Flexibility includes whether the air conditioner has the ability to choose to participate and the ability to withdraw midway. If the air conditioner has one of the above capabilities, the score of the capability index is 100, otherwise it is zero. Finally, the final score of flexibility is obtained by summing the scores of the two capability indicators and the comprehensive weight of the indicators.

[0184] The adjustment gear indicates the number of adjustable modes of the device, and its scoring criteria are as follows: Figure 5 shown.

[0185] The connection mode indicates the communication connection mode of the air conditioner participating in the grid interaction, mainly including the connection between the wifi function and the controller. If the air conditioner has one of the above connection modes, the score of the indicator is 100, otherwise it is zero. Finally, the final score of the connection mode is obtained by multiplying the scores of the two connection modes and the comprehensive weight of the indicators.

[0186] The self-learning ability mainly includes whether the air conditioner has the ability of benefit analysis or self-decision-making. If the air conditioner has one of the above self-learning abilities, the score of the ability index is 100, otherwise it is zero. Finally, the final score of the self-learning ability is obtained by multiplying the scores of the two self-learning ability indicators with the comprehensive weight of the indicators.

[0187] The response modes mainly include managed and self-controlled modes. When the air conditioner has one of the above response modes, the index score of this mode is 100, otherwise it is zero. The final score of the response mode is obtained by multiplying the index scores of the two response modes and the comprehensive weight of the indicators.

[0188] Finally, the first-level indicators of the household air-conditioning demand response capability evaluation index system are obtained by weighting all the corresponding second-level indicators, and the final score of the air-conditioning demand response capability to be evaluated is obtained by weighting all the first-level indicators.

[0189] Embodiment 2:

[0190] A household air conditioner interactive capability evaluation system, the structure is as follows Figure 6 As shown, including:

[0191] A data acquisition module, used to obtain the evaluation data matrix of various indicators of the air conditioner to be evaluated by multiple evaluation objects and the technical parameters of the air conditioner to be evaluated;

[0192] A subjective weight calculation module, used to calculate the subjective weight of each indicator based on the evaluation data matrix using a coefficient of variation correction method;

[0193] An objective weight calculation module, used to calculate the objective weight of each indicator based on the evaluation data matrix using the entropy weight method;

[0194] A comprehensive weight calculation module, used to calculate the subjective weight of each indicator and the objective weight of each indicator using a game theory weighting method to obtain the comprehensive weight of each indicator;

[0195] The interactive capability evaluation module is used to evaluate the interactive capability of the air conditioner to be evaluated based on the comprehensive weights of the indicators and the technical parameters of the air conditioner to be evaluated, and obtain an evaluation result.

[0196] The subjective weight calculation module is specifically used for:

[0197] Performing standardization processing based on the evaluation data matrix to obtain a standardized matrix;

[0198] Calculate the standard deviation and mean value based on the standardized matrix to obtain the standard deviation and mean value of each indicator;

[0199] The coefficient of variation of each indicator was calculated based on the standard deviation and mean value of each indicator to obtain the coefficient of variation of each indicator;

[0200] Based on the coefficient of variation of each indicator, the importance ratio of each indicator to the adjacent indicators is obtained;

[0201] The subjective weight of each indicator is calculated based on the set parameters and the importance ratio of each indicator to its adjacent indicators.

[0202] The calculation formula of the coefficient of variation in the subjective weight calculation module is as follows:

[0203]

[0204] In the formula, C i is the coefficient of variation of the i-th indicator; σ i is the standard deviation of the ith indicator; is the average value of the i-th indicator; i is the ranking of the indicator;

[0205] The calculation formula of the importance ratio of each indicator to the adjacent indicators is as follows:

[0206]

[0207] In the formula, r i is the importance ratio of the ith index to its adjacent indexes; C i-1 is the coefficient of variation of the i-1th indicator; C i is the coefficient of variation of the i-th indicator; i is the ranking of the indicator;

[0208] The calculation formula of the subjective weight of each indicator is as follows:

[0209]

[0210] In the formula, w i is the objective weight of the ith indicator; r o is a parameter; m is the total number of indicators; i is the ranking of indicators;

[0211] w i-1 =r i w i

[0212] In the formula, w i-1 is the subjective weight of the i-1th indicator; r i is the importance ratio between the ith index and its adjacent indexes; w i is the subjective weight of the ith indicator; i is the ranking of the indicator.

[0213] Objective weight calculation module, specifically used for:

[0214] Performing standardization processing based on the evaluation data matrix to obtain a standardized matrix;

[0215] Based on the standardized matrix and each indicator, the proportion of each indicator is calculated to obtain the proportion of each indicator;

[0216] Calculate the evaluation value based on the weight of each indicator to obtain the evaluation value of each indicator;

[0217] The entropy weight is calculated based on the evaluation value of each indicator to obtain the objective weight of each indicator.

[0218] The calculation formula for the weight of each indicator in the objective weight calculation module is as follows:

[0219]

[0220] In the formula, C ij is the weight of the ith indicator; X ij is the standard value of the jth evaluation object for the ith indicator; j is the ranking of the evaluation object; n is the total number of evaluation objects;

[0221] The calculation formula of the evaluation value of each indicator is as follows:

[0222]

[0223] In the formula, h i is the evaluation value of the ith indicator; C ij is the weight of the i-th indicator; j is the ranking of the evaluation objects; n is the total number of evaluation objects;

[0224] The calculation formula of the objective weight of each indicator is as follows:

[0225]

[0226] In the formula, εi is the objective weight of the i-th indicator; i is the indicator ranking; m is the total number of indicators; h i is the evaluation value of the ith indicator.

[0227] Comprehensive weight calculation module, specifically used for:

[0228] Determining a weight vector of each indicator based on the subjective weight of each indicator and the objective weight of each indicator;

[0229] Determine each linear combination coefficient based on the combination formula constructed based on the weight vector of each indicator;

[0230] Perform normalization processing based on each linear combination coefficient to obtain the optimal combination coefficient;

[0231] The optimal combination coefficient is combined with the weight vector of each indicator to calculate and obtain the comprehensive weight of each indicator.

[0232] The process of determining the indicators of the air conditioner to be evaluated in the data acquisition module includes:

[0233] Based on the acquired original indicator set affecting the air conditioning demand response capability to be evaluated, a random sample point, a nearest neighbor sample of the same type as the sample point, and a nearest neighbor sample of a different type from the sample point are selected, and a characteristic weight calculation formula is used to calculate to obtain a weight value of each indicator in the original indicator set;

[0234] Screening is performed based on the weight value of each indicator in the original indicator set to obtain each indicator of the air conditioner to be evaluated.

[0235] The feature weight calculation formula is as follows:

[0236]

[0237] In the formula, W[A i ] is the weight of the i-th feature; A i is the i-th feature; R i is the sample drawn; H j is the jth sample of the same type extracted; a is the number of iterations; b is the number of samples of the same and different types selected; C is the total number of classes different from sample R; M j (c) is the jth nearest neighbor sample of sample R in class C.

[0238] Embodiment 3:

[0239] like Figure 7As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0240] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of a household air conditioner interactive capability evaluation method in the above-mentioned embodiment.

[0241] Example 4

[0242] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a household air conditioner interactive capability evaluation method in the above embodiment.

[0243] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0244] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0245] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0246] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0247] The above are merely embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application are included in the scope of the claims of the present application to be approved.

Claims

1. A method for evaluating the interactive capability of a household air conditioner, characterized in that: include: Obtaining evaluation data matrices of various indicators of the air conditioner to be evaluated by multiple evaluation objects and technical parameters of the air conditioner to be evaluated; Based on the evaluation data matrix, the subjective weight of each indicator is calculated using the coefficient of variation correction method; Based on the evaluation data matrix, the entropy weight method is used to calculate and obtain the objective weight of each indicator; The subjective weight of each indicator and the objective weight of each indicator are calculated using the game theory weighting method to obtain the comprehensive weight of each indicator; The interactive capability of the air conditioner to be evaluated is evaluated based on the comprehensive weights of the indicators and the technical parameters of the air conditioner to be evaluated to obtain an evaluation result.

2. The method according to claim 1, characterized in that The calculation based on the evaluation data matrix using the coefficient of variation correction method to obtain the subjective weight of each indicator includes: Performing standardization processing based on the evaluation data matrix to obtain a standardized matrix; Calculate the standard deviation and mean value based on the standardized matrix to obtain the standard deviation and mean value of each indicator; The coefficient of variation of each indicator was calculated based on the standard deviation and mean value of each indicator to obtain the coefficient of variation of each indicator; Based on the coefficient of variation of each indicator, the importance ratio of each indicator to the adjacent indicators is obtained; The subjective weight of each indicator is calculated based on the set parameters and the importance ratio of each indicator to its adjacent indicators.

3. The method according to claim 2, characterized in that The calculation formula of the coefficient of variation is as follows: In the formula, C i is the coefficient of variation of the i-th indicator; σ i is the standard deviation of the ith indicator; is the average value of the i-th indicator; i is the ranking of the indicator; The calculation formula of the importance ratio of each indicator to the adjacent indicators is as follows: In the formula, r i is the importance ratio of the ith index to its adjacent indexes; C i-1 is the coefficient of variation of the i-1th indicator; C i is the coefficient of variation of the i-th indicator; i is the ranking of the indicator; The calculation formula of the subjective weight of each indicator is as follows: In the formula, w i is the objective weight of the ith indicator; r o is a parameter; m is the total number of indicators; i is the ranking of indicators; w i-1 =r i w i In the formula, w i-1 is the subjective weight of the i-1th indicator; r i is the importance ratio between the ith index and its adjacent indexes; w i is the subjective weight of the ith indicator; i is the ranking of the indicators.

4. The method according to claim 1, characterized in that: The objective weight of each indicator is obtained by calculating based on the evaluation data matrix using the entropy weight method, including: Performing standardization processing based on the evaluation data matrix to obtain a standardized matrix; Based on the standardized matrix and each indicator, the proportion of each indicator is calculated to obtain the proportion of each indicator; Calculate the evaluation value based on the weight of each indicator to obtain the evaluation value of each indicator; The entropy weight is calculated based on the evaluation value of each indicator to obtain the objective weight of each indicator.

5. The method according to claim 4, characterized in that The calculation formula of the proportion of each index is as follows: In the formula, C ij is the weight of the ith indicator; X ij is the standard value of the jth evaluation object for the ith indicator; j is the ranking of the evaluation object; n is the total number of evaluation objects; The calculation formula of the evaluation value of each indicator is as follows: In the formula, h i is the evaluation value of the ith indicator; C ij is the weight of the ith indicator; j is the ranking of the evaluation objects; n is the total number of evaluation objects; The calculation formula of the objective weight of each indicator is as follows: In the formula, ε i is the objective weight of the ith indicator; i is the index ranking; m is the total number of indicators; h i is the evaluation value of the ith indicator.

6. The method according to claim 1, characterized in that The subjective weights based on the indicators and the objective weights of the indicators are calculated using the game theory weighting method to obtain the comprehensive weights of the indicators, including: Determining a weight vector of each indicator based on the subjective weight of each indicator and the objective weight of each indicator; Determine each linear combination coefficient based on the combination formula constructed based on the weight vector of each indicator; Perform normalization processing based on each linear combination coefficient to obtain the optimal combination coefficient; The optimal combination coefficient is combined with the weight vector of each indicator to calculate and obtain the comprehensive weight of each indicator.

7. The method according to claim 1, characterized in that The process of determining the indicators of the air conditioner to be evaluated includes: Based on the acquired original indicator set affecting the air conditioning demand response capability to be evaluated, a random sample point, a nearest neighbor sample of the same type as the sample point, and a nearest neighbor sample of a different type from the sample point are selected, and a characteristic weight calculation formula is used to calculate to obtain a weight value of each indicator in the original indicator set; Screening is performed based on the weight value of each indicator in the original indicator set to obtain each indicator of the air conditioner to be evaluated.

8. The method according to claim 7, characterized in that The feature weight calculation formula is as follows: In the formula, W[A i ] is the weight of the i-th feature; A i is the i-th feature; R i is the sample drawn; H j is the jth sample of the same type extracted; a is the number of iterations; b is the number of samples of the same and different types selected; C is the total number of classes different from sample R; M j (c) is the jth nearest neighbor sample of sample R in class C.

9. A household air conditioner interactive capability evaluation system, characterized in that: include: A data acquisition module, used to obtain the evaluation data matrix of various indicators of the air conditioner to be evaluated by multiple evaluation objects and the technical parameters of the air conditioner to be evaluated; A subjective weight calculation module, used to calculate the subjective weight of each indicator based on the evaluation data matrix using a coefficient of variation correction method; An objective weight calculation module, used to calculate the objective weight of each indicator based on the evaluation data matrix using the entropy weight method; A comprehensive weight calculation module, used to calculate the subjective weight of each indicator and the objective weight of each indicator using a game theory weighting method to obtain the comprehensive weight of each indicator; The interactive capability evaluation module is used to evaluate the interactive capability of the air conditioner to be evaluated based on the comprehensive weights of the indicators and the technical parameters of the air conditioner to be evaluated, and obtain an evaluation result.

10. The system according to claim 9, characterized in that The subjective weight calculation module is specifically used for: Performing standardization processing based on the evaluation data matrix to obtain a standardized matrix; Calculate the standard deviation and mean value based on the standardized matrix to obtain the standard deviation and mean value of each indicator; The coefficient of variation of each indicator was calculated based on the standard deviation and mean value of each indicator to obtain the coefficient of variation of each indicator; Based on the coefficient of variation of each indicator, the importance ratio of each indicator to the adjacent indicators is obtained; The subjective weight of each indicator is calculated based on the set parameters and the importance ratio of each indicator to its adjacent indicators.

11. The system according to claim 10, characterized in that The calculation formula of the coefficient of variation in the subjective weight calculation module is as follows: In the formula, C i is the coefficient of variation of the i-th indicator; σ i is the standard deviation of the ith indicator; is the average value of the i-th indicator; i is the ranking of the indicator; The calculation formula of the importance ratio of each indicator to the adjacent indicators is as follows: In the formula, r i is the importance ratio of the ith index to its adjacent indexes; C i-1 is the coefficient of variation of the i-1th indicator; C i is the coefficient of variation of the i-th indicator; i is the ranking of the indicator; The calculation formula of the subjective weight of each indicator is as follows: In the formula, w i is the objective weight of the ith indicator; r o is a parameter; m is the total number of indicators; i is the ranking of indicators; w i-1 =r i w i In the formula, w i-1 is the subjective weight of the i-1th indicator; r i is the importance ratio between the ith index and its adjacent indexes; w i is the subjective weight of the ith indicator; i is the ranking of the indicators.

12. The system according to claim 9, characterized in that The objective weight calculation module is specifically used for: Performing standardization processing based on the evaluation data matrix to obtain a standardized matrix; Based on the standardized matrix and each indicator, the proportion of each indicator is calculated to obtain the proportion of each indicator; Calculate the evaluation value based on the weight of each indicator to obtain the evaluation value of each indicator; The entropy weight is calculated based on the evaluation value of each indicator to obtain the objective weight of each indicator.

13. The system according to claim 12, characterized in that The calculation formula for the weight of each indicator in the objective weight calculation module is as follows: In the formula, C ij is the weight of the ith indicator; X ij is the standard value of the jth evaluation object for the ith indicator; j is the ranking of the evaluation object; n is the total number of evaluation objects; The calculation formula of the evaluation value of each indicator is as follows: In the formula, h i is the evaluation value of the ith indicator; C ij is the weight of the ith indicator; j is the ranking of the evaluation objects; n is the total number of evaluation objects; The calculation formula of the objective weight of each indicator is as follows: In the formula, ε i is the objective weight of the ith indicator; i is the index ranking; m is the total number of indicators; h i is the evaluation value of the ith indicator.

14. The system according to claim 9, characterized in that The comprehensive weight calculation module is specifically used for: Determining a weight vector of each indicator based on the subjective weight of each indicator and the objective weight of each indicator; Determine each linear combination coefficient based on the combination formula constructed based on the weight vector of each indicator; Perform normalization processing based on each linear combination coefficient to obtain the optimal combination coefficient; The optimal combination coefficient is combined with the weight vector of each indicator to calculate and obtain the comprehensive weight of each indicator.

15. The system according to claim 9, characterized in that The process of determining each index of the air conditioner to be evaluated in the data acquisition module includes: Based on the acquired original indicator set affecting the air conditioning demand response capability to be evaluated, a random sample point, a nearest neighbor sample of the same type as the sample point, and a nearest neighbor sample of a different type from the sample point are selected, and a characteristic weight calculation formula is used to calculate to obtain a weight value of each indicator in the original indicator set; Screening is performed based on the weight value of each indicator in the original indicator set to obtain each indicator of the air conditioner to be evaluated.

16. The system according to claim 15, characterized in that The feature weight calculation formula is as follows: In the formula, W[A i ] is the weight of the i-th feature; A i is the i-th feature; R i is the sample drawn; H j is the jth sample of the same type extracted; a is the number of iterations; b is the number of samples of the same and different types selected; C is the total number of classes different from sample R; M j (c) is the jth nearest neighbor sample of sample R in class C.

17. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a household air conditioner interactive capability evaluation method as described in any one of claims 1 to 8 is implemented.

18. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a household air conditioner interactive capability evaluation method as described in any one of claims 1 to 8 is implemented.

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